There are several considerations for focusing attention on a Great Moderation period. Adrian and Shin (2010) and Jermann and Quadrini (2012) argue that the importance of the nancial sector for the determination of credit and asset prices, which is the main focus of our study, has risen signi cantly during this period. Further, Jermann and Quadrini (2009) discuss a variety of nancial innovations that were taking place or intensi ed in the 1980s, including banking liberalization, and exibility in debt issuance through the introduction of the Asset Backed Securities market. The corporate bond marketrelative to equity marketshas grown tremendously as a source of nance, suggesting that developments in the corporate bond market may more accurately re ect future economic conditions. According to the Securities Industry and Financial Markets Association (SIFMA) over the period 1990 to 2013 the volume of US corporate bonds outstanding more than quantipled from $1.35 trillion to $7.46 trillion. The same body reports that in 2010, total corporate debt was 5.1 times common stock issuance. Philippon (2009) argues that corporate bond spreads may contain news about future corporate fundamentals and provides evidence that information extracted from corporate bond markets, in contrast to the stock market, is informative for U.S. business
xed investment.
Table 13 provides an overview of the data used to construct the observables. All the data transformations we have made in order to construct the dataset used for the estimation of the model are described in detail below. As described in the main body, a subset of variables are used for estimating the various VAR speci cations and they enter in levels. The data series for aggregate utilization adjusted TFP used to estimate the VARs are taken from John Fer-nald's website (www.frbsf.org/economic−research/economists/jfernald/quarterly_tfp.xls), and are described in Fernald (2014).
Sectoral de nition. To allocate a sector to the consumption or investment category, we used the 2005 Input-Output tables. The Input-Output tables track the ows of goods and services across industries and record the nal use of each industry's output into three
Table 13: Time Series used to construct the observables and steady state relationships
Time Series Description Units Code Source
Gross domestic product CP, SA, billion $ GDP BEA
Gross Private Domestic Investment CP, SA, billion $ GPDI BEA
Real Gross Private Domestic Investment CVM, SA, billion $ GPDIC1 BEA
Personal Consumption Exp.: Durable Goods CP, SA, billion $ PCDG BEA
Real Personal Consumption Exp.: Durable Goods CVM, SA, billion $ PCDGCC96 BEA
Personal Consumption Expenditures: Services CP, SA, billion $ PCESV BEA
Real Personal Consumption Expenditures: Services CVM, SA, billion $ PCESVC96 BEA
Personal Consumption Exp.: Nondurable Goods CP, SA, billion $ PCND BEA
Real Personal Consumption Exp.: Nondurable Goods CVM, SA, billion $ PCNDGC96 BEA
Civilian Noninstitutional Population NSA, 1000s CNP160V BLS
Non-farm Business Sector: Compensation Per Hour SA, Index 2005=100 COMPNFB BLS
Non-farm Business Sector: Hours of All Persons SA, Index 2005=100 HOANBS BLS
Eective Federal Funds Rate NSA, percent FEDFUNDS BG
All Employees SA B-1 BLS
Average Weekly Hours SA B-7 BLS
S&P 500 Index Robert Shiller
BAA corporate spread St. Louis FED FRED
GZ Spread Simon Gilchrist
Excess bond premium Simon Gilchrist
Market Equity CRSP
Corporate equity (non nancial corporate sector) Flow of Funds (Z.1 Financial Accounts) FRB
SLOOS FRB
CP = current prices, CVM = chained volume measures (2005 Dollars), SA = seasonally adjusted, NSA = not seasonally adjusted. BEA = U.S. Department of Commerce: Bureau of Economic Analysis, BLS = U.S. Department of Labor: Bureau of Labor Statistics and BG = Board of Governors of the Federal Reserve System, FRB = Federal Reserve Board.
broad categories: consumption, investment and intermediate uses (as well as net exports and government). First, we determine how much of a 2-digit industry's nal output goes to consumption as opposed to investment or intermediate uses.
Then we adopt the following criterion: if the majority of an industry's nal output is allocated to nal consumption demand it is classi ed as a consumption sector; otherwise, if the majority of an industry's output is allocated to investment or intermediate demand, it is classi ed as an investment sector. Using this criterion, mining, utilities, transportation and warehousing, information, manufacturing, construction and wholesale trade industries are classi ed as the investment sector and retail trade, real estate, rental and leasing, professional and business services, educational services, health care and social assistance, arts, entertain-ment, recreation, accommodation and food services and other services except government are classi ed as the consumption sector.41
41The investment sectors' NAICS codes are: 21 22 23 31 32 33 42 48 49 51 (except 491). The consumption sector NAICS codes are: 6 7 11 44 45 53 54 55 56 81. This information is pro-vided by the Bureau of Economic analysis (Use Tables/Before Rede nitions/Producer Value (http :
Real and nominal variables. Consumption (in current prices) is de ned as the sum of personal consumption expenditures on services and personal consumption expenditures on non-durable goods. The times series for real consumption is constructed as follows. First, we compute the shares of services and non-durable goods in total (current price) consumption.
Then, total real consumption growth is obtained as the chained weighted (using the nominal shares above) growth rate of real services and growth rate of real non-durable goods. Using the growth rate of real consumption we construct a series for real consumption using 2005 as the base year. The consumption de ator is calculated as the ratio of nominal over real consumption. In the DSGE model in ation of consumer prices is the growth rate of the consumption de ator. In the VAR model we use the log change in the GDP de ator as our in ation measure, however results are nearly identical when we use the consumption de ator or CPI in ation. Analogously, we construct a time series for the investment de ator using series for (current price) personal consumption expenditures on durable goods and gross private domestic investment and chain weight to arrive at the real aggregate. The relative price of investment is the ratio of the investment de ator and the consumption de ator.
Real output is GDP expressed in consumption units by dividing current price GDP with the consumption de ator.
The hourly wage is de ned as total compensation per hour. Dividing this series by the consumption de ator yields the real wage rate. Hours worked is given by hours of all persons in the non-farm business sector. All series described above as well as the equity capital series (described below) are expressed in per capita terms using the series of non-institutional population, ages 16 and over. The nominal interest rate is the eective federal funds rate. We use the monthly average per quarter of this series and divide it by four to account for the quarterly frequency of the model. The time series for hours is in logs.
Moreover, all series used in estimation (including the nancial time series described below)
//www.bea.gov/industry/io_annual.htm)). We have checked whether there is any migration of 2-digit industries across sectors for our sample. The only industry which changes classi cation (from consumption to investment) during the sample is information which for the majority of the sample can be classi ed as investment and we classify it as such.
are expressed in deviations from their sample average.
Financial variables.
The GZ spread. The GZ spread and excess bond premium series is directly obtained from Simon Gilchrist's website (http : //people.bu.edu/sgilchri/Data/data.htm). The methodol-ogy is described in Gilchrist and Zakrajsek (2012).
The BAA spread. The BAA spread is obtained from the Federal Reserve Bank of St.
Louis online database FRED (https : //fred.stlouisfed.org.).
The S&P 500 index is obtained from Robert Shiller's website (http : //www.econ.yale.edu/ shiller/data.htm) and has been converted to a real per capita index by dividing with the consumption de ator
and non-institutional population, ages 16 and over.
Market equity. The market value of commercial bank's equity is constructed using monthly data from CRSP. From the raw data we retain companies with the following SIC codes to cover the commercial banking sector: 6021 (National Commercial Banks), 6022 (State Com-mercial Banks), 6029 (ComCom-mercial Banks, not elsewhere classi ed), 6081 (Branches and Agencies of Foreign Banks), 6153 (Short-Term Business Credit Institutions, except Agricul-tural), 6159 (Miscellaneous Business Credit Institutions) and 6111 (Federal and Federally-Sponsored Credit Agencies). Market value is calculated as the product of Price (PRC) and Shares Outstanding (SHROUT). We transform the data to quarterly frequency by consider-ing the market value on the last tradconsider-ing day per quarter. The nal series for total equity is generated by taking the log after dividing by Civilian Noninstitutional Population and the consumption de ator.
Senior o cer opinion survey of bank lending practices (SLOOS). The SLOOS is
ob-tained directly from the Federal Reserve (http : //www.federalreserve.gov/datadownload/Choose.aspx?rel = SLOOS). The survey panel contains domestic banks headquartered in all 12 Federal Reserve
Districts, with a minimum of 2 and a maximum of 12 domestic banks in the panel from each district. In general, up to 60 domestically chartered U.S. commercial banks participated in each survey from 1990 through mid-2012; beginning with the July 2012 survey, the size of the domestic panel was increased to include as many as 80 institutions. As described in
the Federal Register Notice authorizing the SLOOS, the panel of domestic respondents as of September 30, 2011 contained 55 banks, 34 of which had assets of $20 billion or more. The combined assets of the respondent banks totaled $7.5 trillion and accounted for 69 percent of the $10.9 trillion in total assets at domestically chartered institutions. The respondent banks also held between 40 percent and 80 percent of total commercial bank loans outstanding in each major loan category regularly queried in the survey, with most categories falling in the upper end of that range. The particular survey question we consider is the net percentage of domestic respondents reporting tightening lending standards for commercial and industry loans for large and medium-sized rms.